The combination of hmax and hogs in an attention guided framework for object localization

ICPRAM 2012 - Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods

Volumn 2, Issue , 2012, Pages 281-288

  Brosch, Tobias ^a   Neumann, Heiko ^a  

^a UNIVERSITY OF ULM   (Germany)

Author keywords

Attention; Combination of HMAX and HOGs; Object localization; Performance evaluation

Indexed keywords

ATTENTION; BIOLOGICALLY INSPIRED; COMBINATION OF HMAX AND HOGS; FEATURE DISTRIBUTION; FEATURE-BASED; HIERARCHICAL METHOD; HISTOGRAMS OF ORIENTED GRADIENTS; INPUT IMAGE; OBJECT DETECTION; OBJECT LOCALIZATION; PERFORMANCE EVALUATION; PERFORMANCE MEASURE; VISUAL OBJECT RECOGNITION; VISUAL SYSTEMS;

IMAGE SEGMENTATION; KNOWLEDGE BASED SYSTEMS;

OBJECT RECOGNITION;

EID: 84862165877     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (23)

1. Agarwal, S., Awan, A., and Roth, D. (2004a). Learning to Detect Objects in Images via a Sparse, Part-Based Representation. TPAMI, 26(11):1475-1490.
2. Agarwal, S., Awan, A., and Roth, D. (2004b). UIUC Image Database for Car Detection download page. http://l2r.cs.uiuc.edu/-cogcomp/Data/Car/[Online; accessed 27-Mar.-2010].
3. Amit, Y., Geman, D., and Fan, X. (2004). A Coarse-to- Fine Strategy for Multiclass Shape Detection. TPAMI, 26(12):1606-21.
4. An, S., Peursum, P., Liu, W., Venkatesh, S., and Chen, X. (2010). Exploiting Monge Structures in Optimum Subwindow Search. In CVPR.
5. bwGRID (2011). member of the German D-Grid initiative, funded by the Ministry for Education and Research (Bundesministerium f̈ur Bildung und Forschung) and the Ministry for Science, Research and Arts Baden-Wuerttemberg (Ministerium f̈ur Wissenschaft, Forschung und Kunst Baden-Ẅurttemberg). http://www.bw-grid.de [Online; accessed 13- Apr.-2011].
6. Dalal, N. and Triggs, B. (2005). Histograms of Oriented Gradients for Human Detection. In CVPR, volume 1, pages 886-893.
7. Enzweiler, M. and Gavrila, D. M. (2009). Monocular Pedestrian Detection: Survey and Experiments. TPAMI, 31(12):2179-95.
8. Fan, R., Chang, K.-W., Hsieh, C.-J., Wang, X.-R., and Lin, C.-J. (2008). LIBLINEAR: A Library for Large Linear Classification. JMLR, 9:1871-1874.
9. Fradkin, D. and Muchnik, I. (2006). Support Vector Machines for Classification. DIMACS Series in Discrete Mathematics and Theoretical Computer Science, 70:13-20.
10. Freund, Y. and Schapire, R. E. (1997). A Decision- Theoretic Generalization of On-Line Learning and an Application to Boosting. Journal of Computer and System Sciences, 55:119-139.
11. Fritz, M., Leibe, B., Caputo, B., and Schiele, B. (2005). Integrating Representative and Discriminative Models for Object Category Detection. In ICCV.
12. Hamker, F. H. (2005). The Emergence of Attention by Population-based Inference and its Role in Distributed Processing and Cognitive Control of Vision. Computer Vision and Image Understanding, 100:64-106.
13. Heisele, B., Serre, T., Mukherjee, S., and Poggio, T. (2001). Feature Reduction and Hierarchy of Classifiers for Fast Object Detection in Video Images. In CVPR.
14. Jiang, X., Rosen, E., Zeffiro, T., Vanmeter, J., Blanz, V., and Riesenhuber, M. (2006). Evaluation of a Shape-Based Model of Human Face Discrimination using FMRI and Behavioral Techniques. Neuron, 50(1):159-72.
15. Lampert, C. H., Blaschko, M. B., and Hofmann, T. (2008). Beyond Sliding Windows: Object Localization by Efficient Subwindow Search. In CVPR, pages 1-8.
16. Mutch, J. and Lowe, D. G. (2008). Object Class Recognition and Localization using sparse Features with limited Receptive Fields. IJCV, 80(1):45-57.
17. Pedersoli, M., Gonz̀alez, J., Bagdanov, A. D., and Villanueva, J. J. (2010). Recursive Coarse-to-Fine Localization for fast Object Detection. In ECCV, volume 6.
18. Riesenhuber, M. and Poggio, T. (1999a). Are Cortical Models Really Bound by the "Binding Problem"? Neuron, 24:87-93.
19. Riesenhuber, M. and Poggio, T. (1999b). Hierarchical Models of Object Recognition in Cortex. Nature Neuroscience, 2(11):1019-1025.
20. Schyns, P. G. and Oliva, A. (1994). From Blobs To Boundary Edges: Evidence for Time- and Spatial-Scale- Dependent Scene Recognition. Psychological Science, 5(4):195-200.
21. Serre, T., Wolf, L., and Poggio, T. (2005). Object Recognition with Features inspired by Visual Cortex. In CVPR, pages 994-1000.
22. Viola, P. and Michael, J. (2001). Rapid Object Detection using a Boosted Cascade of Simple Features. In CVPR.
23. Zhu, Q., Avidan, S., Yeh, M.-C., and Cheng, K.-T. (2006). Fast Human Detection Using a Cascade of Histograms of Oriented Gradients. In CVPR.